Improving manufacturing plant performance through the use of advanced digital technologies imposes stringent requirements on the quality of sensor data. Validated sensor data is a prerequisite for any method which seeks to improve operator awareness of plant conditions, such as thermal state and equipment condition. The dominant problem among the different data-driven sensor validation methods that presently exist relates to the high-false alarm rate. The origin of false alarms include the inability of many methods to perform extrapolation, the inability of many methods to operate with data where plant dynamics have been excited or otherwise perturbed and the absence of guidelines for how the measurement vector should be composed or what is an appropriate set of training data to ensure the physical behavior of the system is adequately captured. These root causes of false alarms are in need of improvement.
One particularly important example of a commercial plant operation which requires rigid and thorough performance is a nuclear power plant which requires accurate and reliable indications of process variable values to operate at peak performance and under safe conditions. Achieving maximum availability, power output, and safety requires a high degree of confidence that the outputs from sensors accurately represent the underlying physical process-variable values. A faulty reading can lead to inappropriate operator actions that can result in either unnecessary thermal cycling of equipment or inadvertent actuation of safety systems. The extreme operating conditions that sensors operate in can, however, result in structural deterioration of a sensor with time, eventually causing the measurement to become unreliable. From the standpoint of safe and efficient operation, there is a need to detect failing sensors so that maintenance can be performed and the quality of sensors readings assured to provide the desired peak performance and safe operating conditions.
The trend toward advanced operator aids places even more stringent requirements on sensor viability and reliability. Situational awareness algorithms for improving operator perception of the plant condition for better managing operation will require validated sensor readings as will semi-automated fault recovery procedures. Sensor values will need to be tested for correctness and shown to satisfy a criterion for acceptability, possibly quantified with a maximum permissible error.
Current industry practice for detecting failing sensors is ad hoc, time consuming, and presents a significant mental challenge to the operator. The operator must scan thousands of sensor readings and correlate these with his own mental model for the underlying physical processes. There is a need to automate sensor validation and to do it more reliably than is achievable by an operator.
Sensor degradation manifests itself as a de-calibration or response time deterioration of the sensor output signal. The early literature on sensor aging identified environmental stress factors giving rise to age-related changes. Heat, humidity, vibration, temperature cycling, and mechanical shock are important drivers of age-related change for resistance temperature detectors (“RTD”). Over time these environmental variables can induce changes in the resistance of insulation, oxidation of the sensing element from long-term exposure at high temperatures, and ingress of moisture. These changes in material properties give rise to the observed sensor aging-related changes. More recently sensor degradation has come to be regarded as a materials problem. From the point of view of materials science, environmental driving potentials can cause atoms to diffuse across material interfaces, cracks and porosity to develop in the bulk, and individual atoms to transmute.
The origin of the false alarms can also include inability of many algorithms to perform extrapolation, inability of many algorithms to operate with data where plant dynamics have been excited, and absence of guidelines for how the measurement vector should be composed or for what is an appropriate set of training data to ensure the physical behavior of the system is adequately captured. It is therefore important to develop a capability that addresses the above root causes of false alarms and is able to detect sensor degradation and correct the sensor output until such time as the sensor can be either re-calibrated or replaced, such as during a planned shutdown. The latter point further recognizes that sensors are not readily accessible for maintenance during operation and is another problem source demanding an improved system and method for providing solutions to the various problems set forth hereinbefore.